www.neuron.yale.edu

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// load nrngui
load_file("nrngui.hoc")

// define cell template
begintemplate Cell
public soma
create soma
proc init(){
    soma {
        diam = 12.6157
        L = 12.6157
        nseg = 5
        cm = 1
        Ra = 100
        
        insert pas
        e_pas = -65
        g_pas = 0.001

        insert hh2
        gnabar_hh2 = 0.12
        gkbar_hh2 = 0.036
        vtraub_hh2 = -64.2

        insert im
        gkbar_im = 0.000001

        ena = 50
        ek = -90
    }
}
endtemplate Cell

// make cell object
objref cell
cell = new Cell()

// make step current input
objref stim
stim = new IClamp(0.5)
stim.del = 1100
stim.dur = 2
stim.amp = 1

// set graph of voltage trace
objref g
g = new Graph()
g.addexpr("cell.soma.v(0.5)")
g.size(1000, 1300, -100, 50)
graphList[0].append(g)

// set simulation parameter & run
v_init = -65
dt = 0.2
tstop = 2000
celsius = 30
run()

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# import library
from neuron import h, gui
import matplotlib.pyplot as plt

# define cell class
class Cell():
    def __init__(self):
        super().__init__()
        self.soma = h.Section(name = 'soma', cell = self)
        self.soma.diam = 12.6157
        self.soma.L = 12.6157
        self.soma.nseg = 5
        self.soma.cm = 1
        self.soma.Ra = 100
        
        self.soma.insert('pas')
        for seg in self.soma:
            seg.pas.e = -65
            seg.pas.g = 0.001
        self.soma.insert('hh2')
        for seg in self.soma:
            seg.hh2.gnabar = 0.12
            seg.hh2.gkbar = 0.036
            seg.hh2.vtraub = -64.2
        self.soma.insert('im')
        for seg in self.soma:
            seg.im.gkbar = 0.000001
        self.soma.ena = 50
        self.soma.ek = -90

# make cell object
cell = Cell()

# make step current input
stim = h.IClamp(cell.soma(0.5))
stim.delay = 1100
stim.dur = 2
stim.amp = 1

# set recorder for graph
tvec = h.Vector().record(h._ref_t)
vvec = h.Vector().record(cell.soma(0.5)._ref_v)

# set simulation parameter & run
h.finitialize(-65)
h.dt = 0.2
h.continuerun(2000)
h.celsius = 30

# plot voltage trace
plt.figure()
plt.plot(tvec, vvec)
plt.xlim(1000, 1300)
plt.ylim(-100, 50)
plt.show()

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TITLE Hippocampal HH channels
:
: Fast Na+ and K+ currents responsible for action potentials
: Iterative equations
:
: Equations modified by Traub, for Hippocampal Pyramidal cells, in:
: Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991
:
: range variable vtraub adjust threshold
:
: Written by Alain Destexhe, Salk Institute, Aug 1992
:
: Modified Oct 96 for compatibility with Windows: trap low values of arguments
:
: Modifications by Arthur Houweling for use in MyFirstNEURON
: Modifications by Paulo Aguiar: vh changed from 5 to 6 - NOT ANYMORE: vh=5 as originally set

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX hh2
	USEION na READ ena WRITE ina
	USEION k READ ek WRITE ik
	RANGE gnabar, gkbar, vtraub
	RANGE m_inf, h_inf, n_inf
	RANGE tau_m, tau_h, tau_n
	RANGE m_exp, h_exp, n_exp
	RANGE ik, ina 
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

PARAMETER {
	gnabar	= .1 	(mho/cm2)
	gkbar	= .06 	(mho/cm2)

	ena		(mV)
	ek		(mV)
	celsius		(degC)
	dt              (ms)
	v               (mV)
	vtraub	= -55	(mV)	: adjusts threshold
}

STATE {
	m h n
}

ASSIGNED {
	ina	(mA/cm2)
	ik	(mA/cm2)
	il	(mA/cm2)
	m_inf
	h_inf
	n_inf
	tau_m
	tau_h
	tau_n
	m_exp
	h_exp
	n_exp
	tadj
}


BREAKPOINT {
	SOLVE states METHOD cnexp
	ina = gnabar * m*m*m*h * (v - ena)
	ik  = gkbar * n*n*n*n * (v - ek)
}


DERIVATIVE states {   : use this for exact Hodgkin-Huxley equations
	evaluate_fct(v)
	m' = (m_inf - m) / tau_m
	h' = (h_inf - h) / tau_h
	n' = (n_inf - n) / tau_n
}

:PROCEDURE states() {	: this discretized form is more stable
:	evaluate_fct(v)
:	m = m + m_exp * (m_inf - m)
:	h = h + h_exp * (h_inf - h)
:	n = n + n_exp * (n_inf - n)
:	VERBATIM
:	return 0;
:	ENDVERBATIM
:}

UNITSOFF
INITIAL {
:
:  Q10 was assumed to be 3 for both currents
:
	tadj = 3.0 ^ ((celsius-36)/ 10 )
	evaluate_fct(v)
	m= m_inf
	h= h_inf
	n= n_inf
}

PROCEDURE evaluate_fct(v(mV)) { LOCAL a,b,v2, vh

	v2 = v - vtraub : convert to traub convention
	vh = 0		: Original Value = 5
	
:	a = 0.32 * (13-v2) / ( exp((13-v2)/4) - 1)
	a = 0.32 * vtrap(13-v2, 4)

:	b = 0.28 * (v2-40) / ( exp((v2-40)/5) - 1)
	b = 0.28 * vtrap(v2-40, 5)

	tau_m = 1 / (a + b) / tadj
	m_inf = a / (a + b)

	:a = 0.128 * exp((17-v2)/18)
	:b = 4 / ( 1 + exp((40-v2)/5) )
	:tau_h = 1 / (a + b) / tadj
	:h_inf = a / (a + b)
	
	a = 0.128 * exp((17-v2-vh)/18)
	b = 4 / ( 1 + exp((40-v2-vh)/5) )
	tau_h = 1 / (a + b) / tadj
	h_inf = a / (a + b)	
	
	
	: a = 0.032 * (15-v2) / ( exp((15-v2)/5) - 1)
	a = 0.032 * vtrap(15-v2, 5)	
	b = 0.5 * Exp((10-v2)/40)
	tau_n = 1 / (a + b) / tadj
	n_inf = a / (a + b)


	m_exp = 1 - Exp(-dt/tau_m)
	h_exp = 1 - Exp(-dt/tau_h)
	n_exp = 1 - Exp(-dt/tau_n)
}

FUNCTION vtrap(x,y) {
	if (fabs(x/y) < 1e-6) {
		vtrap = y*(1 - x/y/2)
	}else{
		vtrap = x/(Exp(x/y)-1)
	}
}

FUNCTION Exp(x) {
	if (x < -100) {
		Exp = 0
	}else{
		Exp = exp(x)
	}
} 

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TITLE Cortical M current
:
:   M-current, responsible for the adaptation of firing rate and the 
:   afterhyperpolarization (AHP) of cortical pyramidal cells
:
:   First-order model described by hodgkin-Hyxley like equations.
:   K+ current, activated by depolarization, noninactivating.
:
:   Model taken from Yamada, W.M., Koch, C. and Adams, P.R.  Multiple 
:   channels and calcium dynamics.  In: Methods in Neuronal Modeling, 
:   edited by C. Koch and I. Segev, MIT press, 1989, p 97-134.
:
:   See also: McCormick, D.A., Wang, Z. and Huguenard, J. Neurotransmitter 
:   control of neocortical neuronal activity and excitability. 
:   Cerebral Cortex 3: 387-398, 1993.
:
:   Written by Alain Destexhe, Laval University, 1995
:

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX im
	USEION k READ ek WRITE ik
    RANGE gkbar, ik, m_inf, tau_m
	GLOBAL taumax

}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}


PARAMETER {
	v		(mV)
	celsius = 36    (degC)
	ek		(mV)
	gkbar	= 1e-6	(mho/cm2)
	taumax	= 1000	(ms)		: peak value of tau
}



STATE {
	m
}

ASSIGNED {
	ik	(mA/cm2)
	m_inf
	tau_m	(ms)
	tau_peak	(ms)
	tadj
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ik = gkbar * m * (v - ek)
}

DERIVATIVE states { 
	evaluate_fct(v)

	m' = (m_inf - m) / tau_m
}

UNITSOFF
INITIAL {
	evaluate_fct(v)
	m = 0
:
:  The Q10 value is assumed to be 2.3
:
        tadj = 2.3 ^ ((celsius-36)/10)
	tau_peak = taumax / tadj
}

PROCEDURE evaluate_fct(v(mV)) {

	m_inf = 1 / ( 1 + exptable(-(v+35)/10) )
	tau_m = tau_peak / ( 3.3 * exptable((v+35)/20) + exptable(-(v+35)/20) )
}
UNITSON


FUNCTION exptable(x) { 
	TABLE  FROM -25 TO 25 WITH 10000

	if ((x > -25) && (x < 25)) {
		exptable = exp(x)
	} else {
		exptable = 0.
	}
}

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h.finitialize(-65)
h.dt = 0.2
h.continuerun(50)
h.celsius = 30

Code: Select all

h.finitialize(-65)
h.dt = 0.2
h.celsius = 30
h.continuerun(50)